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last update in 2024

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王庆刚
2024-12-31 16:45:04 +08:00
parent dac3b3f2b6
commit 7e13e0f5b4
20 changed files with 1349 additions and 389 deletions
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108
contrast/one2n_contrast.py
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@ -13,25 +13,23 @@ from scipy.spatial.distance import cdist
from utils.event import ShoppingEvent
def gen_eventdict(sourcePath, stype="data"):
def init_eventdict(sourcePath, stype="data"):
'''stype: str,
'source': 由 videos 或 images 生成的 pickle 文件
'data': 从 data 文件中读取的现场运行数据
'''
k, errEvents = 0, []
for source_path in sourcePath:
evtpath, bname = os.path.split(source_path)
for bname in os.listdir(sourcePath):
# bname = r"20241126-135911-bdf91cf9-3e9a-426d-94e8-ddf92238e175_6923555210479"
source_path = os.path.join(evtpath, bname)
source_path = os.path.join(sourcePath, bname)
if not os.path.isdir(source_path): continue
pickpath = os.path.join(eventDataPath, f"{bname}.pickle")
if os.path.isfile(pickpath): continue
try:
event = ShoppingEvent(source_path, stype)
# save_data(event, resultPath)
with open(pickpath, 'wb') as f:
pickle.dump(event, f)
@ -44,7 +42,7 @@ def gen_eventdict(sourcePath, stype="data"):
# break
errfile = os.path.join(resultPath, 'error_events.txt')
with open(errfile, 'w', encoding='utf-8') as f:
with open(errfile, 'a', encoding='utf-8') as f:
for line in errEvents:
f.write(line + '\n')
@ -61,6 +59,44 @@ def read_eventdict(eventDataPath):
return evtDict
def simi_calc(event, o2nevt, typee=None):
if typee == "11":
boxes1 = event.front_boxes
boxes2 = o2nevt.front_boxes
feat1 = event.front_feats
feat2 = o2nevt.front_feats
if typee == "10":
boxes1 = event.front_boxes
boxes2 = o2nevt.back_boxes
feat1 = event.front_feats
feat2 = o2nevt.back_feats
if typee == "00":
boxes1 = event.back_boxes
boxes2 = o2nevt.back_boxes
feat1 = event.back_feats
feat2 = o2nevt.back_feats
if typee == "01":
boxes1 = event.back_boxes
boxes2 = o2nevt.front_boxes
feat1 = event.back_feats
feat2 = o2nevt.front_feats
if len(feat1) and len(feat2):
matrix = 1 - cdist(feat1[0], feat2[0], 'cosine')
simi = np.mean(matrix)
else:
simi = None
return simi
def one2n_pr(evtDicts):
@ -81,45 +117,22 @@ def one2n_pr(evtDicts):
o2nevt = o2n_evt[0]
else:
continue
if typee == "11":
boxes1 = event.front_trackingboxes
boxes2 = o2nevt.front_trackingboxes
feat1 = event.front_trackingfeats
feat2 = o2nevt.front_trackingfeats
if typee == "10":
boxes1 = event.front_trackingboxes
boxes2 = o2nevt.back_trackingboxes
feat1 = event.front_trackingfeats
feat2 = o2nevt.back_trackingfeats
if typee == "00":
boxes1 = event.back_trackingboxes
boxes2 = o2nevt.back_trackingboxes
feat1 = event.back_trackingfeats
feat2 = o2nevt.back_trackingfeats
if typee == "01":
boxes1 = event.back_trackingboxes
boxes2 = o2nevt.front_trackingboxes
feat1 = event.back_trackingfeats
feat2 = o2nevt.front_trackingfeats
matrix = 1 - cdist(feat1[0], feat2[0], 'cosine')
simi_mean = np.mean(matrix)
simi_max = np.max(matrix)
simival = simi_calc(event, o2nevt, typee)
if simival==None:
continue
evt_names.append(nname)
evt_barcodes.append(barcode)
evt_similars.append(simi_mean)
evt_similars.append(simival)
evt_types.append(typee)
if len(evt_names)==len(evt_barcodes) and len(evt_barcodes)==len(evt_similars) \
and len(evt_similars)==len(evt_types) and len(evt_names)>0:
maxsim = evt_similars[evt_similars.index(max(evt_similars))]
# maxsim = evt_similars[evt_similars.index(max(evt_similars))]
maxsim = max(evt_similars)
for i in range(len(evt_names)):
bcd, simi = evt_barcodes[i], evt_similars[i]
@ -173,16 +186,16 @@ def one2n_pr(evtDicts):
plt.show()
## ============================= 1:n 直方图'''
fig, axes = plt.subplots(2, 2)
axes[0, 0].hist(tpsimi, bins=60, edgecolor='black')
axes[0, 0].hist(tpsimi, bins=60, range=(-0.2, 1), edgecolor='black')
axes[0, 0].set_xlim([-0.2, 1])
axes[0, 0].set_title('TP')
axes[0, 1].hist(fpsimi, bins=60, edgecolor='black')
axes[0, 1].hist(fpsimi, bins=60, range=(-0.2, 1), edgecolor='black')
axes[0, 1].set_xlim([-0.2, 1])
axes[0, 1].set_title('FP')
axes[1, 0].hist(tnsimi, bins=60, edgecolor='black')
axes[1, 0].hist(tnsimi, bins=60, range=(-0.2, 1), edgecolor='black')
axes[1, 0].set_xlim([-0.2, 1])
axes[1, 0].set_title('TN')
axes[1, 1].hist(fnsimi, bins=60, edgecolor='black')
axes[1, 1].hist(fnsimi, bins=60, range=(-0.2, 1), edgecolor='black')
axes[1, 1].set_xlim([-0.2, 1])
axes[1, 1].set_title('FN')
plt.show()
@ -192,16 +205,16 @@ def one2n_pr(evtDicts):
def main():
'''1. 生成事件字典并保存至 eventDataPath, 只需运行一次 '''
# gen_eventdict(sourcePath)
# init_eventdict(eventSourcePath)
'''2. 读取时间字典 '''
'''2. 读取事件字典 '''
evtDicts = read_eventdict(eventDataPath)
'''3. 1:n 比对事件评估 '''
fpevents = one2n_pr(evtDicts)
fpErrFile = str(Path(resultPath).joinpath("one2n_Error.txt"))
fpErrFile = str(Path(resultPath).joinpath("one2n_fp_Error.txt"))
with open(fpErrFile, "w") as file:
for item in fpevents:
file.write(item + "\n")
@ -214,8 +227,7 @@ def main():
if __name__ == '__main__':
eventSourcePath = [r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\images"]
eventSourcePath = r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\images"
resultPath = r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\result"
eventDataPath = os.path.join(resultPath, "evtobjs")